Microsoft today announced the general availability of the Azure SQL Data Warehouse, an elastic data warehouse as a service with enterprise-class features. It is a fully managed DW as a Service that you can provision in minutes and scale up to 60 times larger in seconds. With Azure SQL Data Warehouse, storage and compute scale independently. You can dynamically deploy, grow, shrink, and even pause compute, taking advantage of best-in-class price/performance. Also, SQL Data Warehouse uses the power and familiarity of T-SQL to let you easily integrate query results across relational data in your data warehouse and non-relational data in Azure blob storage.

In the event of a c-section, if a robot suggested who should perform it, would you listen? Ninety percent of the time, nurses and physicians did. A robot programmed by MIT's Computer Science and Artificial Intelligence Lab (CSAIL) suggests where to move patients and who should perform caesarian sections. The team from CSAIL thinks robots are most effective in helping with one of the most "complex" tasks in the labor ward of the hospital: scheduling. "The aim of the work was to develop artificial intelligence that can learn from people about how the labor and delivery unit works, so that robots can better anticipate how to be helpful or when to stay out of the way--and maybe even help by collaborating in making challenging decisions," says MIT professor Julie Shah, senior author on two papers based on CSAIL's research.

In what is being called the "first beer brewed by artificial intelligence," IntelligentX hopes to create products which are constantly evolving by bringing their entire client base and expert brewers together via its Automated Brewing Intelligence (ABI, pronounced "Abbey") algorithm.

An anonymous reader quotes a report from Reuters: A former Google engineer, whose speech recognition software is used in more than a billion Android smartphones, has launched a company that uses blockchain technology to build a new operating system for banks. Paul Taylor, a Cambridge University academic with an expertise in artificial intelligence, speech synthesis and machine learning, started working on the system, called Vault OS, two years ago in a basement in London's Shoreditch district, known for being a tech start-up hub. The technology, which underpins the digital currency bitcoin, creates a shared database in which participants can trace every transaction ever made. The ledger is tamper-proof and transparent, meaning that transactions can be processed without the need for third-party verification. The system also negates the need for costly in-house data centers, as it uses cloud-based systems, which banks can use on a "pay-as-you-go" basis, which means that there is no single point of failure.

Diagnosing cancer is about to get more accurate, with the help of artificial intelligence. Pathologists have diagnosed diseases in more or less the same way for the past 100 years, by laboring over a microscope reviewing biopsy samples on little glass slides. Working almost robotically, they sift through millions of normal cells to identify just a few diseased ones. The task is tedious and prone to human error. But now, scientists and engineers have created a technique that uses artificial intelligence (AI) and can differentiate cancer cells from normal cells almost as well as a top-notch pathologist.

IntelligentX is a beer you don't necessarily need to love on the first go. That's because it evolves with every batch based on customer feedback, as Smithsonian reports. Billed as the "world's first beer brewed by artificial intelligence," it uses a Facebook messenger bot to incorporate customer preferences into new batches of beer, tweaking its recipe along the way to learn how to make better brews. London-based marketers at 10x teamed up with their neighbors at the machine-learning startup Intelligent Layer to add "launch fancy new beer" to the former's portfolio, so there's a reason it looks so slick. Food and booze are usually positioned as the artistic result of a chef or brewmaster's craft, but IntelligentX makes it, essentially, a scientific equation.

Broadly speaking, educational activities can be split into two categories: life skills and professional skills. The life skills that we all need to learn, and the way we learn them, have remained relatively consistent across the ages: how to communicate, socialize and survive. But you can argue that today's education system is skewed toward the second category, the teaching of professional skills and it's this category that will face the greatest opportunities and challenges over the next 50 years. While educators prepare students for lives of learning, it's more true to say their role is to prepare students for lifelong careers. But while that was a relatively simple task in the past, it's now much more difficult.

In particular, we consider the mixture of two approaches: Parametric modeling based on rigid body dynamics equations and nonparametric modeling based on incremental kernel methods, with no prior information on the mechanical properties of the system. We validate the proposed technique learning the dynamics of one arm of the iCub humanoid robot. Persistence weighted Gaussian kernel for topological data analysis by Genki Kusano, Kenji Fukumizu, Yasuaki Hiraoka Topological data analysis is an emerging mathematical concept for characterizing shapes in multi-scale data. In this field, persistence diagrams are widely used as a descriptor of the input data, and can distinguish robust and noisy topological properties. Nowadays, it is highly desired to develop a statistical framework on persistence diagrams to deal with practical data.

Machine learning is fundamental for start-ups today because it translates your business-related problems into ML algorithms. Some common business issues like product recommendation, fraud detection, and ad targeting feature "standard" machine learning formulas that have brought tremendous results. There are hundreds, maybe thousands of other formulations in ML that lead to increased predictive accuracy. However, app developers are not scientists. A single developer can't do it all as machine learning is an incredibly vast technology – it offers tools that can be exploited endlessly.

Microsoft R Server, the most broadly deployable enterprise-class analytics platform for R available today, is generally available. With a variety of big-data capabilities, including statistics, predictive modeling, and machine learning, R Server supports the full range of analytics based on open-source R: exploration, analysis, visualization, and modeling. By using and extending open-source R, R Server is fully compatible with R scripts, functions, and CRAN packages and can analyze data at enterprise scale. We also addressed in-memory limitations of open-source R by adding parallel and chunked data processing in R Server, so users can run analytics on data much bigger than what fits in main memory. R Server delivers enterprise-class performance and scalability for your R-based applications with libraries that you can use to write once and deploy across multiple platforms with minimal effort, whether on-premises or in the cloud.